International Journal of Distributed and Cloud Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Fractional and Self-Adaptive Autoregressive Dragonfly Optimization for Privacy Preserved Data Publishing in Mobile Cloud Computing


Affiliations
1 Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana, India
     

   Subscribe/Renew Journal


The advancement in Mobile Cloud Computing (MCC) has gained immense knowledge in computing concept for upcoming generation. The wireless communi-cations enable the integration of cloud computing and mobile to generate MCC. Privacy and security are the major issues faced by MCC while publishing data. This work introduces a technique, named Self-Adaptive Autoregressive Dragonfly Optimization (S-ADO), for addressing the issues by determining the secret key optimally using retrievable data perturbation technique for privacy preserved data publishing in MCC. The retrievable data perturbation is performed using fractional theory and matrix product based model with proposed S-ADO. The proposed S-ADO is developed by modifying ADO by making it self-adaptive. Initially, a fitness function is computed using privacy & utility parameters for determining the optimal differential derivative coefficients. The optimal coefficients are used to generate the secret key by using fractional theory. Then the matrix product based model is adapted to convert original data into privacy preserved data. The secret key derived using utility and privacy functions, is also used to recover the original data. The performance of the proposed S-ADO algorithm shows superior performance with privacy and utility values as 0.7855, and 0.7088 respectively.

Keywords

Data Perturbation, Fractional Theory, MCC, Self-Adaptive, Utility.
Subscription Login to verify subscription
User
Notifications
Font Size


  • C. Tang, S. Xiao, X. Wei, M. Hao, and W. Chen, “Energy efficient and deadline satisfied task scheduling in mobile cloud computing,” in Proceedings of IEEE International Conference on Big Data and Smart Computing, pp. 198-205, 2018.

  • T. Wang, X. Wei, T. Liang, and J. Fan, “Dynamic tasks scheduling based on weighted bi-graph in mobile cloud computing,” Sustainable Computing: Informatics and Systems, vol. 19, pp. 214-222, 2018.

  • C. Tang, M. Hao, X. Wei, and W. Chen, “Energy-aware task scheduling in mobile cloud computing,” Distributed and Parallel Databases, vol. 36, no. 3, pp. 529-553, 2018.

  • T. Li, Z. Liu, J. Li, C. Jia, and K.-C. Li, “CDPS: A cryptographic data publishing system,” Journal of Computer and System Sciences, vol. 89, pp. 80-91, 2017.

  • M. Sharma, A. Chaudhary, M. Mathuria, and S. Chaudhary, “A review study on the privacy preserving data mining techniques and approaches,” International Journal of Computer Science and Telecommunications, vol. 4, no. 9, pp. 42-46, 2013.

  • J. J. Panackal, A. S. Pillai, and V. N. Krishnachandran, “Disclosure risk of individuals: A k-anonymity study on health care data related to Indian population,” in Proceedings of International Conference on Data Science & Engineering, pp. 200-205, August 2014.

  • S. Ni, M. Xie, and Q. Qian, “Clustering based k-anonymity algorithm for privacy preservation,” International Journal of Network Security, vol. 19, no. 6, pp. 1062-1071, 2017.

  • M. M. A. Alphonsa, and P. Amudhavalli, “Genetically modified glowworm swarm optimization based privacy preservation in cloud computing for healthcare sector,” Evolutionary Intelligence, vol. 11, no. 1-2, pp. 101-116, 2018.

  • P. Samarati, “Protecting respondents’ identities in microdata release,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 6, pp. 1010-1027, 2001.

  • L. Sweeney, “K-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570, 2002.

  • X. Xiao, and Y. Tao, “Anatomy: Simple and effective privacy preservation,” in Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 139-150, 2006.

  • R. Nallakumar, N. Sengottaiyan, and M. M. Arif, “Cloud computing and methods for privacy preservation: A survey,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 3, no. 11, pp. 3752-3756, 2014.

  • K. S. S. R. Yarrapragada, and B. B. Krishna, “Impact of tamanu oil-diesel blend on combustion, performance and emissions of diesel engine and its prediction methodology,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, pp. 1-15, 2015.

  • A. Sarkar, and T. S. Murugan, “Cluster head selection for energy efficient and delay-less routing in wireless sensor network,” Wireless Networks, pp. 1-18, 2017.

  • R. S. Begum, and R. Sugumar, “Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud,” Cluster Computing, pp. 1-8, 2017.

  • T. Paigude, and T. A. Chavan, “A survey on privacy preserving public auditing for data storage security,” International Journal of Computer Trends and Technology, vol. 4, no. 3, pp. 412-418, 2013.

  • H. Hammami, H. Brahmi, I. Brahmi, and S. B. Yahia, “Using homomorphic encryption to compute privacy preserving data mining in a cloud computing environment,” in Proceedings of European, Mediterranean, and Middle Eastern Conference on Information Systems, pp. 397-413, September 2017.

  • R. H. Jadhav, “Distributed bottom up approach for data anonymization using MapReduce framework on cloud,” International Journal of Advance Scientific Research and Engineering Trends, vol. 3, no. 6, pp. 109-113, 2018.

  • Z. Wang, X. Pang, Y. Chen, H. Shao, Q. Wang, L. Wu, H. Chen, and H. Qi, “Privacy-preserving crowd-sourced statistical data publishing with an untrusted server,” IEEE Transactions on Mobile Computing, 2018.

  • F. Yu, M. Chen, B. Yu, W. Li, L. Ma, and H. Gao, “Privacy preservation based on clustering perturbation algorithm for social network,” Multimedia Tools and Applications, vol. 77, no. 9, pp. 11241-11258, 2018.

  • K. C. Sreedhar, M. N. Faruk, and B. Venkateswarlu, “A genetic TDS and BUG with pseudo-identifier for privacy preservation over incremental data sets,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2863-2873, 2017.

  • T. Kalidoss, G. Sannasi, S. Lakshmanan, K. Kanagasabai, and A. Kannan, “Data anonymisation of vertically partitioned data using Map Reduce techniques on cloud,” International Journal of Communication Networks and Distributed Systems, vol. 20, no. 4, pp. 519-531, 2018.

  • J. Li, J. Wei, W. Liu, and X. Hu, “PMDP: A framework for preserving multiparty data privacy in cloud computing,” Security and Communication Networks, 2017.

  • X. Zhang, W. Dou, J. Pei, S. Nepal, C. Yang, C. Liu, and J. Chen, “Proximity-aware local-recoding anonymization with MapReduce for scalable big data privacy preservation in cloud,” IEEE Transactions on Computers, vol. 64, no. 8, pp. 2293-2307, September 2014.

  • S. Mirjalili, “Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Computing and Applications, vol. 27, no. 4, pp. 1053-1073, May 2016.

  • R. F. Engle, and S. Manganelli, “CAViaR: Conditional value at risk by quantile regression,” Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pp. 367-381, October 1999.

  • N. P. Karlekar, and N. Gomathi, “Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud,” International Journal of Modeling, Simulation, and Scientific Computing, vol. 8, no. 3, 1750021, 2017.

  • Bank Marketing Data Set. Available: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

  • DBworld e-mails data set. Available: https://www.openml.org/d/1564

  • E. J. S. Pires, J. A. T. Machado, P. B. de M. Oliveira, J. B. Cunha, and L. Mendes, “Particle swarm optimization with fractional-order velocity,” Nonlinear Dynamics, vol. 61, no. 1-2, pp. 295-301, July 2010.

  • A. George, and A. Sumathi, “Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud,” Cluster Computing, pp. 1-12, 2018.


Abstract Views: 341

PDF Views: 0




  • Fractional and Self-Adaptive Autoregressive Dragonfly Optimization for Privacy Preserved Data Publishing in Mobile Cloud Computing

Abstract Views: 341  |  PDF Views: 0

Authors

Matish Garg
Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana, India
Rajender Nath
Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana, India

Abstract


The advancement in Mobile Cloud Computing (MCC) has gained immense knowledge in computing concept for upcoming generation. The wireless communi-cations enable the integration of cloud computing and mobile to generate MCC. Privacy and security are the major issues faced by MCC while publishing data. This work introduces a technique, named Self-Adaptive Autoregressive Dragonfly Optimization (S-ADO), for addressing the issues by determining the secret key optimally using retrievable data perturbation technique for privacy preserved data publishing in MCC. The retrievable data perturbation is performed using fractional theory and matrix product based model with proposed S-ADO. The proposed S-ADO is developed by modifying ADO by making it self-adaptive. Initially, a fitness function is computed using privacy & utility parameters for determining the optimal differential derivative coefficients. The optimal coefficients are used to generate the secret key by using fractional theory. Then the matrix product based model is adapted to convert original data into privacy preserved data. The secret key derived using utility and privacy functions, is also used to recover the original data. The performance of the proposed S-ADO algorithm shows superior performance with privacy and utility values as 0.7855, and 0.7088 respectively.

Keywords


Data Perturbation, Fractional Theory, MCC, Self-Adaptive, Utility.

References